{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f47f0bdf",
   "metadata": {},
   "source": [
    "#### Preprocess the SHP dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4dcd61",
   "metadata": {},
   "source": [
    "#### Import necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44296a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "import dask.dataframe as dd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cd4ff4",
   "metadata": {},
   "source": [
    "#### Load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3607dcbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start loading CSV file...\n",
      "\n",
      "Finish loading CSV file...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = None\n",
    "\n",
    "column_names = [\"idx\", \"ecg_id\", \"lead1\", \"lead2\", \"lead3\", \"aVR\", \"aVL\", \"aVF\",\n",
    "                \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"arrhythmia_code\"]\n",
    "dtypes = {\"ecg_id\": \"str\", \"lead1\" : \"float64\", \"lead2\" : \"float64\", \"lead3\" : \"float64\", \"aVR\" : \"float64\", \"aVL\" : \"float64\",\n",
    "          \"aVF\" : \"float64\", \"V1\" : \"float64\", \"V2\" : \"float64\", \"V3\" : \"float64\", \"V4\" : \"float64\",\n",
    "          \"V5\" : \"float64\", \"V6\" : \"float64\", \"arrhythmia_code\" : \"int16\"}\n",
    "\n",
    "try:\n",
    "    print(\"\\nStart loading CSV file...\")\n",
    "    df = dd.read_csv(\"../dataset/csv_files/ecg_sph_dataset.csv\", sep=\"|\", dtype = dtypes)\n",
    "    df = df.drop(columns = [\"idx\"])\n",
    "    print(\"\\nFinish loading CSV file...\")\n",
    "except Exception as e:\n",
    "    print(\"\\nFail to load CSV file.\")\n",
    "    print(\"Error: {}\".format(e))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb29b14",
   "metadata": {},
   "source": [
    "#### Display dataset size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2aaa7fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128850000, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.compute().shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84160098",
   "metadata": {},
   "source": [
    "#### Display dataset data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57902a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ecg_id              object\n",
       "lead1              float64\n",
       "lead2              float64\n",
       "lead3              float64\n",
       "aVR                float64\n",
       "aVL                float64\n",
       "aVF                float64\n",
       "V1                 float64\n",
       "V2                 float64\n",
       "V3                 float64\n",
       "V4                 float64\n",
       "V5                 float64\n",
       "V6                 float64\n",
       "arrhythmia_code      int16\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0f0c8f",
   "metadata": {},
   "source": [
    "#### Display the first rows of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "594b3180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ecg_id</th>\n",
       "      <th>lead1</th>\n",
       "      <th>lead2</th>\n",
       "      <th>lead3</th>\n",
       "      <th>aVR</th>\n",
       "      <th>aVL</th>\n",
       "      <th>aVF</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>arrhythmia_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A00001</td>\n",
       "      <td>0.0216064453125000</td>\n",
       "      <td>0.1303710937500</td>\n",
       "      <td>0.10882568359375</td>\n",
       "      <td>-0.07598876953125</td>\n",
       "      <td>-0.043212890625000</td>\n",
       "      <td>0.11999511718750</td>\n",
       "      <td>-0.0111999511718750</td>\n",
       "      <td>-0.0064010620117187</td>\n",
       "      <td>-0.0351867675781250</td>\n",
       "      <td>-0.054412841796875</td>\n",
       "      <td>-0.037597656250000</td>\n",
       "      <td>-0.062408447265625</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A00001</td>\n",
       "      <td>0.0216064453125000</td>\n",
       "      <td>0.1319580078125</td>\n",
       "      <td>0.10961914062500</td>\n",
       "      <td>-0.07678222656250</td>\n",
       "      <td>-0.044006347656250</td>\n",
       "      <td>0.12078857421875</td>\n",
       "      <td>-0.0120010375976562</td>\n",
       "      <td>-0.0103988647460937</td>\n",
       "      <td>-0.0335998535156250</td>\n",
       "      <td>-0.054412841796875</td>\n",
       "      <td>-0.036804199218750</td>\n",
       "      <td>-0.062408447265625</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A00001</td>\n",
       "      <td>0.0207977294921875</td>\n",
       "      <td>0.1319580078125</td>\n",
       "      <td>0.11120605468750</td>\n",
       "      <td>-0.07678222656250</td>\n",
       "      <td>-0.044799804687500</td>\n",
       "      <td>0.12158203125000</td>\n",
       "      <td>-0.0111999511718750</td>\n",
       "      <td>-0.0136032104492187</td>\n",
       "      <td>-0.0312042236328125</td>\n",
       "      <td>-0.052795410156250</td>\n",
       "      <td>-0.036804199218750</td>\n",
       "      <td>-0.062408447265625</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A00001</td>\n",
       "      <td>0.0184020996093750</td>\n",
       "      <td>0.1328125000000</td>\n",
       "      <td>0.11358642578125</td>\n",
       "      <td>-0.07598876953125</td>\n",
       "      <td>-0.047210693359375</td>\n",
       "      <td>0.12322998046875</td>\n",
       "      <td>-0.0103988647460937</td>\n",
       "      <td>-0.0151977539062500</td>\n",
       "      <td>-0.0272064208984375</td>\n",
       "      <td>-0.051208496093750</td>\n",
       "      <td>-0.036804199218750</td>\n",
       "      <td>-0.063171386718750</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A00001</td>\n",
       "      <td>0.0160064697265625</td>\n",
       "      <td>0.1335449218750</td>\n",
       "      <td>0.11682128906250</td>\n",
       "      <td>-0.07440185546875</td>\n",
       "      <td>-0.050415039062500</td>\n",
       "      <td>0.12561035156250</td>\n",
       "      <td>-0.0080032348632812</td>\n",
       "      <td>-0.0167999267578125</td>\n",
       "      <td>-0.0223999023437500</td>\n",
       "      <td>-0.048797607421875</td>\n",
       "      <td>-0.035186767578125</td>\n",
       "      <td>-0.064025878906250</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ecg_id               lead1            lead2             lead3  \\\n",
       "0  A00001  0.0216064453125000  0.1303710937500  0.10882568359375   \n",
       "1  A00001  0.0216064453125000  0.1319580078125  0.10961914062500   \n",
       "2  A00001  0.0207977294921875  0.1319580078125  0.11120605468750   \n",
       "3  A00001  0.0184020996093750  0.1328125000000  0.11358642578125   \n",
       "4  A00001  0.0160064697265625  0.1335449218750  0.11682128906250   \n",
       "\n",
       "                aVR                aVL               aVF                  V1  \\\n",
       "0 -0.07598876953125 -0.043212890625000  0.11999511718750 -0.0111999511718750   \n",
       "1 -0.07678222656250 -0.044006347656250  0.12078857421875 -0.0120010375976562   \n",
       "2 -0.07678222656250 -0.044799804687500  0.12158203125000 -0.0111999511718750   \n",
       "3 -0.07598876953125 -0.047210693359375  0.12322998046875 -0.0103988647460937   \n",
       "4 -0.07440185546875 -0.050415039062500  0.12561035156250 -0.0080032348632812   \n",
       "\n",
       "                   V2                  V3                 V4  \\\n",
       "0 -0.0064010620117187 -0.0351867675781250 -0.054412841796875   \n",
       "1 -0.0103988647460937 -0.0335998535156250 -0.054412841796875   \n",
       "2 -0.0136032104492187 -0.0312042236328125 -0.052795410156250   \n",
       "3 -0.0151977539062500 -0.0272064208984375 -0.051208496093750   \n",
       "4 -0.0167999267578125 -0.0223999023437500 -0.048797607421875   \n",
       "\n",
       "                  V5                 V6  arrhythmia_code  \n",
       "0 -0.037597656250000 -0.062408447265625               22  \n",
       "1 -0.036804199218750 -0.062408447265625               22  \n",
       "2 -0.036804199218750 -0.062408447265625               22  \n",
       "3 -0.036804199218750 -0.063171386718750               22  \n",
       "4 -0.035186767578125 -0.064025878906250               22  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pd.set_option(\"display.precision\", 16)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd614fa4",
   "metadata": {},
   "source": [
    "#### Display dataset statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e584809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lead1</th>\n",
       "      <th>lead2</th>\n",
       "      <th>lead3</th>\n",
       "      <th>aVR</th>\n",
       "      <th>aVL</th>\n",
       "      <th>aVF</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>arrhythmia_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "      <td>128850000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>35.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.243</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.554</td>\n",
       "      <td>1.151</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.957</td>\n",
       "      <td>1.497</td>\n",
       "      <td>51.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-52.219</td>\n",
       "      <td>-17.375</td>\n",
       "      <td>-572.000</td>\n",
       "      <td>-285.500</td>\n",
       "      <td>-52.500</td>\n",
       "      <td>-285.750</td>\n",
       "      <td>-277.250</td>\n",
       "      <td>-1006.000</td>\n",
       "      <td>-1016.500</td>\n",
       "      <td>-704.500</td>\n",
       "      <td>-854.000</td>\n",
       "      <td>-1146.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.066</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.014</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.016</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>125.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>573.000</td>\n",
       "      <td>15.820</td>\n",
       "      <td>52.812</td>\n",
       "      <td>25.812</td>\n",
       "      <td>573.000</td>\n",
       "      <td>26.688</td>\n",
       "      <td>626.500</td>\n",
       "      <td>559.500</td>\n",
       "      <td>622.000</td>\n",
       "      <td>618.000</td>\n",
       "      <td>501.000</td>\n",
       "      <td>828.500</td>\n",
       "      <td>166.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              lead1         lead2         lead3           aVR           aVL  \\\n",
       "count 128850000.000 128850000.000 128850000.000 128850000.000 128850000.000   \n",
       "mean         -0.000         0.000         0.000        -0.000        -0.000   \n",
       "std           0.243         0.139         0.243         0.156         0.233   \n",
       "min         -52.219       -17.375      -572.000      -285.500       -52.500   \n",
       "25%          -0.034        -0.046        -0.025        -0.008        -0.018   \n",
       "50%          -0.014        -0.017         0.000         0.026        -0.002   \n",
       "75%           0.016         0.026         0.026         0.050         0.020   \n",
       "max         573.000        15.820        52.812        25.812       573.000   \n",
       "\n",
       "                aVF            V1            V2            V3            V4  \\\n",
       "count 128850000.000 128850000.000 128850000.000 128850000.000 128850000.000   \n",
       "mean          0.000        -0.002        -0.001        -0.002        -0.002   \n",
       "std           0.156         0.554         1.151         1.286         0.825   \n",
       "min        -285.750      -277.250     -1006.000     -1016.500      -704.500   \n",
       "25%          -0.032        -0.011        -0.046        -0.066        -0.075   \n",
       "50%          -0.008         0.022         0.006        -0.014        -0.028   \n",
       "75%           0.022         0.060         0.086         0.059         0.036   \n",
       "max          26.688       626.500       559.500       622.000       618.000   \n",
       "\n",
       "                 V5            V6  arrhythmia_code  \n",
       "count 128850000.000 128850000.000    128850000.000  \n",
       "mean         -0.002        -0.002           35.589  \n",
       "std           0.957         1.497           51.926  \n",
       "min        -854.000     -1146.000            1.000  \n",
       "25%          -0.068        -0.056            1.000  \n",
       "50%          -0.031        -0.026           22.000  \n",
       "75%           0.024         0.018          125.000  \n",
       "max         501.000       828.500          166.000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = df.drop(columns = [\"ecg_id\"])\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "df.describe().compute()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb6d672",
   "metadata": {},
   "source": [
    "#### Check for missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd86dd07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dask.dataframe.core.DataFrame'>\n",
      "Index: 128850000 entries, 0 to 274303\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype\n",
      "---  ------           --------------  -----\n",
      " 0   lead1            128850000 non-null      float64\n",
      " 1   lead2            128850000 non-null      float64\n",
      " 2   lead3            128850000 non-null      float64\n",
      " 3   aVR              128850000 non-null      float64\n",
      " 4   aVL              128850000 non-null      float64\n",
      " 5   aVF              128850000 non-null      float64\n",
      " 6   V1               128850000 non-null      float64\n",
      " 7   V2               128850000 non-null      float64\n",
      " 8   V3               128850000 non-null      float64\n",
      " 9   V4               128850000 non-null      float64\n",
      "10   V5               128850000 non-null      float64\n",
      "11   V6               128850000 non-null      float64\n",
      "12   arrhythmia_code  128850000 non-null      int16\n",
      "dtypes: float64(12), int16(1)\n",
      "memory usage: 11.8 GB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df.info(verbose = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0400a994",
   "metadata": {},
   "source": [
    "\n",
    "#### Check for skewness.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06fd3bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lead1              1328.123\n",
       "lead2                -0.504\n",
       "lead3             -1322.843\n",
       "aVR                -624.924\n",
       "aVL                1505.720\n",
       "aVF                -624.883\n",
       "V1                  474.870\n",
       "V2                 -169.419\n",
       "V3                  -88.185\n",
       "V4                  210.849\n",
       "V5                 -131.622\n",
       "V6                  -11.211\n",
       "arrhythmia_code       1.316\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.skew().compute()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4654437",
   "metadata": {},
   "source": [
    "#### Check for kurtosis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bddbe30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lead1             2685193.053\n",
       "lead2                 496.022\n",
       "lead3             2661075.195\n",
       "aVR                984716.674\n",
       "aVL               3169260.441\n",
       "aVF                973723.078\n",
       "V1                 457993.434\n",
       "V2                 199325.968\n",
       "V3                 147437.222\n",
       "V4                 216492.585\n",
       "V5                 139920.472\n",
       "V6                  54436.510\n",
       "arrhythmia_code         0.125\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.kurtosis().compute()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fe22ca",
   "metadata": {},
   "source": [
    "#### Generate the correlation matrix between the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "812f0c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 lead1  lead2  lead3    aVR    aVL    aVF     V1     V2  \\\n",
      "lead1            1.000  0.282 -0.838 -0.904  0.959 -0.527 -0.186 -0.049   \n",
      "lead2            0.282  1.000  0.288 -0.665 -0.003  0.667 -0.119 -0.007   \n",
      "lead3           -0.838  0.288  1.000  0.524 -0.959  0.905  0.118  0.045   \n",
      "aVR             -0.904 -0.665  0.524  1.000 -0.745  0.113  0.198  0.041   \n",
      "aVL              0.959 -0.003 -0.959 -0.745  1.000 -0.747 -0.159 -0.049   \n",
      "aVF             -0.527  0.667  0.905  0.113 -0.747  1.000  0.039  0.031   \n",
      "V1              -0.186 -0.119  0.118  0.198 -0.159  0.039  1.000  0.124   \n",
      "V2              -0.049 -0.007  0.045  0.041 -0.049  0.031  0.124  1.000   \n",
      "V3              -0.001  0.075  0.044 -0.032 -0.024  0.067  0.061  0.060   \n",
      "V4               0.009  0.194  0.102 -0.093 -0.049  0.165  0.078  0.072   \n",
      "V5              -0.005  0.224  0.132 -0.096 -0.072  0.202  0.012  0.031   \n",
      "V6              -0.025  0.118  0.092 -0.033 -0.061  0.123  0.026  0.009   \n",
      "arrhythmia_code -0.000  0.000  0.000  0.000 -0.000  0.000  0.000  0.000   \n",
      "\n",
      "                    V3     V4     V5     V6  arrhythmia_code  \n",
      "lead1           -0.001  0.009 -0.005 -0.025           -0.000  \n",
      "lead2            0.075  0.194  0.224  0.118            0.000  \n",
      "lead3            0.044  0.102  0.132  0.092            0.000  \n",
      "aVR             -0.032 -0.093 -0.096 -0.033            0.000  \n",
      "aVL             -0.024 -0.049 -0.072 -0.061           -0.000  \n",
      "aVF              0.067  0.165  0.202  0.123            0.000  \n",
      "V1               0.061  0.078  0.012  0.026            0.000  \n",
      "V2               0.060  0.072  0.031  0.009            0.000  \n",
      "V3               1.000  0.157  0.046  0.019            0.000  \n",
      "V4               0.157  1.000 -0.068  0.016            0.000  \n",
      "V5               0.046 -0.068  1.000  0.171            0.001  \n",
      "V6               0.019  0.016  0.171  1.000            0.001  \n",
      "arrhythmia_code  0.000  0.000  0.001  0.001            1.000  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "correlation_matrix = df.corr(numeric_only = True).compute()\n",
    "print(correlation_matrix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623b978e-79e8-4483-abdc-b7331acee27a",
   "metadata": {},
   "source": [
    "#### Show correlation ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18ccf56b-299f-44c6-bde8-7c653664c892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aVF              aVF               1.000\n",
      "aVR              aVR               1.000\n",
      "V5               V5                1.000\n",
      "V6               V6                1.000\n",
      "V2               V2                1.000\n",
      "V4               V4                1.000\n",
      "arrhythmia_code  arrhythmia_code   1.000\n",
      "aVL              aVL               1.000\n",
      "V3               V3                1.000\n",
      "lead2            lead2             1.000\n",
      "lead3            lead3             1.000\n",
      "V1               V1                1.000\n",
      "lead1            lead1             1.000\n",
      "aVL              lead3             0.959\n",
      "lead3            aVL               0.959\n",
      "aVL              lead1             0.959\n",
      "lead1            aVL               0.959\n",
      "aVF              lead3             0.905\n",
      "lead3            aVF               0.905\n",
      "lead1            aVR               0.904\n",
      "aVR              lead1             0.904\n",
      "lead3            lead1             0.838\n",
      "lead1            lead3             0.838\n",
      "aVL              aVF               0.747\n",
      "aVF              aVL               0.747\n",
      "aVL              aVR               0.745\n",
      "aVR              aVL               0.745\n",
      "lead2            aVF               0.667\n",
      "aVF              lead2             0.667\n",
      "aVR              lead2             0.665\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "correlation_ranking = correlation_matrix.abs().unstack().sort_values(ascending = False)\n",
    "print(correlation_ranking[:30])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071d9ad5",
   "metadata": {},
   "source": [
    "#### Generate the lead1 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c0cda6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='lead1'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhMAAAGwCAYAAAATw+f5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcHklEQVR4nO3de5DVdf348dfugqArCwoJ4oJRXrJERVDSLKkcrLzkNNNYLmK3aWzSILTUGsXRMZi8hDVlkzqWI4V/iI3ZDZrELk4UKCNaY5QWXmIYRV3UhNp9f//ot+fHwp7ds/vaZZF9PGaYYc75fN6f93ntZw9P112oK6WUAADoo/rB3gAA8MYmJgCAFDEBAKSICQAgRUwAACliAgBIERMAQMqwvp7Y3t4ezz33XIwaNSrq6ur6c08AwAAppcTWrVtj4sSJUV/fP19T6HNMPPfcczFp0qR+2QQAsHs9/fTT0dzc3C9r9TkmRo0aVdlMU1NTv2wGABhYra2tMWnSpMqf4/2hzzHR8b82mpqaxAQAvMH057co+AZMACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYqIGL774YsyaNStmzZoVL7744mBvBwD2KGICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTNSgvb29y98DAGKiJq2trV3+HgAQEwBAkpgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApAwb7A3s7KGHHorrrrsuXn311cHeSpc++clPDvYWYtasWXH11Vf3y1oPPfRQ3HzzzTFv3rw4+eSTezwmIuLrX/96REScddZZsWLFim7PzVw3IuL222+PpUuXRktLS3z605/u8txa1+rp2l093t3ate6jluN6Onf27Nl9nnVfZtHbY3pzXC3H9/W5/jZQ869l3d7cp9m9dHxOf/nLX+73mQ7EfjPrZc7v7ax6ula194bZs2fH8uXL47XXXos5c+ZU3vv2ZHWllNKXE1tbW2P06NHx8ssvR1NTU79s5vXXX4/zzjsvtmzZ0i/r7c3uvvvuGD9+fGqN119/PebMmRPPP/98jBs3Lu66664YOXJk1WPGjh0bpZTKx6euri5KKVXPzVw3IuKll16Kj3zkI9He3h719fWxfPnyGDlyZKdzb7vttvjMZz7T41o9XburxyOi6j53Pr7aPmo5rtp1djy3vr4+2tvbez3r7Meh1o9VrcfVcnxfn+tvAzX/WtbtzX2a3UtLS0u88MILERExduzYWLp0ab/NdCD2m1kvc35vZ9XTtbp7b9hRXV1d3HvvvTFmzJiaX2dPBuLP7z3qf3MsXbpUSNTo4osvTq+xdOnSyifGCy+8ED/84Q97PGbHj09Hh1Y7N3PdiIgrr7wy2tvbIyKivb09rrrqql3OvfLKK2taq6drd/V4d/usdR+1HFfLnjrm0NtZ92UWvT2mN8fVcnxfn+tvAzX/WtbtzX3aX3vprzWrrd/f++3Lepnzezurnq7V3XvDjkopcdVVV9W8z8Gyx3xl4plnnom5c+dWPrno2eWXXx4f+MAH+nTuM888ExdccEG0tbVVHhs2bFh8//vfj+bm5qrHVLPzuZnrRkSsWbMmLr300l3O7/gvub7so9q1Fy9eHJdddlmnxxsaGiIiutxnRPQ4l2rr7qzadbo7t9ZZd6evH/+url3rcbUcH7HrbGt5LjOLrnR372euWcu6EV2/zq7uiYHYS0NDQ/zgBz9Iz7S398VAr5c5v9qfT9Vm1dO1evPe2uGGG26IGTNm1Hx8dwb1KxPbtm2L1tbWTr/6Syklbr75ZiHRS9dff32vbsYOHfOu9ngppeox1bS3t1fOzVy3Y61rrrmm6nW6s/NatVz7mmuu2eX4tra2XWbbscaSJUu63UNPr6GW61xzzTVVX2u111irzMd/52vXelwt116yZEm3a3U19+wsutLTvV/Lvd7XdbubQVf3aV9ff8d5Xb1/tLW1xZIlS1Iz7e19MdDrZc7vuDe7+nxsa2vb5fyertVx//RWd+8Je4KaY2LRokUxevToyq9Jkyb12yY2btwYf/rTn/ptvaGira0t7r///l6f1zHvnd9I2tra4k9/+lNs3Lix6jHVtLe3V87NXDciYvXq1X2O1Z3XquXara2tNX2Sdqy9Zs2aHufS3t4era2tfYq9jj1Ve4Or9hprlfn473ztWo+r5dpr1qzpdq2u5p6dRVd6uvdrudf7um53M+jqPu3r6+/pPXfNmjWpmfb2vhjo9TLnb9y4MdasWVP1+Z3P7+laq1ev7tV7a4fW1tZYvXp1r87ZnWqOiSuuuCJefvnlyq+nn3663zYxefLkOOGEE/ptvaGioaEhzjzzzF6f1zHvji+x77jeiSeeGJMnT656TDX19fWVczPXjYiYOXNmn7/0tvNatVy7qakp6ut7/lToWHvGjBk9zqW+vj6amppqnl9Xe6qrq+t2H93NujuZj//O1671uFqufcIJJ3S7Vldzz86iKz3d+7Xc631dt7sZdHWf9vX19/See8IJJ6Rm2tv7YqDXy5w/efLkbv/3ws7n93StmTNn9uq9tcPo0aNj5syZvTpnd6o5JkaMGBFNTU2dfvWXurq6mDdvXk1v6Px/l112WZ/+sOqYd7XH6+rqqh5TTX19feXczHU71qr2DUc93SM7r1XLtRcuXLjL8Q0NDbvMtmON+fPnd7uHjn0uXLiwx+OqXWfhwoVVX2u111irzMd/52vXelwt154/f363a3U19+wsutLTvV/Lvd7XdbubQVf3aV9ff8d5Xb1/NDQ0xPz581Mz7e19MdDrZc7vuDe7+nxsaGjY5fyertVx//RWd+8Je4I9ZmfNzc3R0tIy2Nt4wzjooINi9uzZfT6/ubk5zjvvvE5/KJx33nlxyCGHVD2mmq7OzVw3ImLGjBkxderUTo8dc8wx0dLS0uncqVOn9rhWT9eePn36Lo+3tLRU3WdX63S1j67W3fm4atfZ+dy+zLo7ffn4V7t2rcfVcnxfn+tv1e797DVrWbc392l/7GVnLS0t/TLT/v54ZdfLnF/tz6dqs+rpWj29h+zsmGOOieOPP76m1zlY9piYiPjfB+bAAw8c7G28IXzrW99Kr9HS0hJjx46NiIhx48ZVfWPZ8ZgdPz4dN361czPXjYi49tprKyVeX18f11xzzS7nXnvttTWt1dO1u3q8u33Wuo9ajqtlTx1z6O2s+zKL3h7Tm+NqOb6vz/W3gZp/Lev25j7tr73015rV1u/v/fZlvcz5vZ1VT9fq7r1hRx3vfXu6PSomRo4cGZdeemk0NjYO9lb2aLNmzUr/hVUR/5v3ggULYvz48fHFL36xy798ZcdjFixYEJdeemmMGTMmxowZE3PmzOn23Mx1IyLGjBkTLS0tUV9fHy0tLTFmzJhdzh0zZkxNa/V07a4e726fte6jluNq2VNLS0ufZt2XWfT2mN4cV8vxfX2uvw3U/GtZtzf3aXYvl1xySeVzesGCBf0604HYb2a9zPm9nVVP1+ruveH888+PxsbGylcv+/MvrBooe8zfM7Ene+qppyp/jfYdd9wRU6ZMGeQdAUDf7PV/AyYA8MYjJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVM1KCpqanL3wMAYqIm9fX1Xf4eABATAECSmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApwwZ7A28EBxxwQKxatWqwtwEAeyRfmQAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAApYgIASBETAECKmAAAUsQEAJAiJgCAFDEBAKSICQAgRUwAACliAgBIERMAQIqYAABSxAQAkCImAICUYX09sZQSERGtra39thkAYGB1/Lnd8ed4f+hzTGzdujUiIiZNmtRvmwEAdo+tW7fG6NGj+2WtutLHNGlvb4/nnnsuRo0aFXV1df2ymT1Za2trTJo0KZ5++uloamoa7O3sccyne+bTPfPpmRl1z3y6t+N8Ro0aFVu3bo2JEydGfX3/fLdDn78yUV9fH83Nzf2yiTeSpqYmN2o3zKd75tM98+mZGXXPfLrXMZ/++opEB9+ACQCkiAkAIEVM1GjEiBGxcOHCGDFixGBvZY9kPt0zn+6ZT8/MqHvm072Bnk+fvwETACDCVyYAgCQxAQCkiAkAIEVMAAApYqJG3/nOd2LKlCkxcuTImD59evz2t78d7C0NuN/85jdx1llnxcSJE6Ouri5+/OMfd3q+lBJXX311TJw4Mfbdd9+YNWtWPP74452O2bZtW1x88cUxbty4aGxsjLPPPjueeeaZ3fgqBs6iRYvihBNOiFGjRsVBBx0U55xzTjzxxBOdjhnKM7rlllvimGOOqfwlOSeddFL8/Oc/rzw/lGfTlUWLFkVdXV3Mnz+/8thQntHVV18ddXV1nX5NmDCh8vxQnk2HZ599NubMmRNjx46N/fbbL4477rhYu3Zt5fndOqNCj5YtW1aGDx9ebr311vLnP/+5zJs3rzQ2NpZ//vOfg721AfWzn/2sfPWrXy333HNPiYhy7733dnp+8eLFZdSoUeWee+4p69evL+eee245+OCDS2tra+WYCy+8sBxyyCFl5cqV5eGHHy7vfe97y7HHHlv++9//7uZX0/9OP/30cscdd5THHnusrFu3rpxxxhll8uTJ5ZVXXqkcM5RndN9995Wf/vSn5YknnihPPPFE+cpXvlKGDx9eHnvssVLK0J7Nzv74xz+WN7/5zeWYY44p8+bNqzw+lGe0cOHC8o53vKP861//qvzavHlz5fmhPJtSStmyZUs59NBDyyc+8YmyevXq8tRTT5Vf/epX5W9/+1vlmN05IzFRgxNPPLFceOGFnR5729veVi6//PJB2tHut3NMtLe3lwkTJpTFixdXHnv99dfL6NGjy3e/+91SSikvvfRSGT58eFm2bFnlmGeffbbU19eXX/ziF7tt77vL5s2bS0SUBx98sJRiRl054IADym233WY2O9i6dWs5/PDDy8qVK8upp55aiYmhPqOFCxeWY489tsvnhvpsSinlsssuK6ecckrV53f3jPxvjh5s37491q5dG7Nnz+70+OzZs+Ohhx4apF0Nvqeeeio2bdrUaS4jRoyIU089tTKXtWvXxn/+859Ox0ycODGOPvrovXJ2L7/8ckREHHjggRFhRjtqa2uLZcuWxauvvhonnXSS2ezg85//fJxxxhlx2mmndXrcjCI2bNgQEydOjClTpsTHPvaxePLJJyPCbCIi7rvvvpgxY0Z89KMfjYMOOiimTZsWt956a+X53T0jMdGD559/Ptra2mL8+PGdHh8/fnxs2rRpkHY1+Dpee3dz2bRpU+yzzz5xwAEHVD1mb1FKiQULFsQpp5wSRx99dESYUUTE+vXrY//9948RI0bEhRdeGPfee2+8/e1vN5v/Z9myZfHwww/HokWLdnluqM9o5syZceedd8Yvf/nLuPXWW2PTpk1x8sknxwsvvDDkZxMR8eSTT8Ytt9wShx9+ePzyl7+MCy+8ML7whS/EnXfeGRG7//7p878aOtTs/M+sl1KGxD+93pO+zGVvnN1FF10Ujz76aPzud7/b5bmhPKMjjzwy1q1bFy+99FLcc889ccEFF8SDDz5YeX4oz+bpp5+OefPmxYoVK2LkyJFVjxuqM/rgBz9Y+f3UqVPjpJNOire+9a3xgx/8IN75zndGxNCdTUREe3t7zJgxI772ta9FRMS0adPi8ccfj1tuuSXmzp1bOW53zchXJnowbty4aGho2KXSNm/evEvxDSUd31Xd3VwmTJgQ27dvjxdffLHqMXuDiy++OO6777544IEHorm5ufK4GUXss88+cdhhh8WMGTNi0aJFceyxx8bNN99sNvG/LzFv3rw5pk+fHsOGDYthw4bFgw8+GN/85jdj2LBhldc4lGe0o8bGxpg6dWps2LDB/RMRBx98cLz97W/v9NhRRx0VGzdujIjd//4jJnqwzz77xPTp02PlypWdHl+5cmWcfPLJg7SrwTdlypSYMGFCp7ls3749Hnzwwcpcpk+fHsOHD+90zL/+9a947LHH9orZlVLioosuiuXLl8evf/3rmDJlSqfnzWhXpZTYtm2b2UTE+9///li/fn2sW7eu8mvGjBnR0tIS69ati7e85S1DfkY72rZtW/zlL3+Jgw8+2P0TEe9617t2+VH0v/71r3HooYdGxCC8//Tq2zWHqI4fDb399tvLn//85zJ//vzS2NhY/vGPfwz21gbU1q1byyOPPFIeeeSREhHlpptuKo888kjlR2IXL15cRo8eXZYvX17Wr19fPv7xj3f5Y0fNzc3lV7/6VXn44YfL+973vr3mR7M+97nPldGjR5dVq1Z1+vG11157rXLMUJ7RFVdcUX7zm9+Up556qjz66KPlK1/5Sqmvry8rVqwopQzt2VSz409zlDK0Z3TJJZeUVatWlSeffLL84Q9/KGeeeWYZNWpU5X13KM+mlP/9OPGwYcPKddddVzZs2FCWLl1a9ttvv3LXXXdVjtmdMxITNfr2t79dDj300LLPPvuU448/vvLjf3uzBx54oETELr8uuOCCUsr/fvRo4cKFZcKECWXEiBHlPe95T1m/fn2nNf7973+Xiy66qBx44IFl3333LWeeeWbZuHHjILya/tfVbCKi3HHHHZVjhvKMPvWpT1U+Z970pjeV97///ZWQKGVoz6aanWNiKM+o4+9EGD58eJk4cWL5yEc+Uh5//PHK80N5Nh1+8pOflKOPPrqMGDGivO1tbyvf+973Oj2/O2fknyAHAFJ8zwQAkCImAIAUMQEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBOxlZs2aFfPnz3/DXwN44xATQNry5cvj9NNPj3HjxkVdXV2sW7dusLcE7EZiAkh79dVX413velcsXrx4sLcCDAIxAXux7du3x5e//OU45JBDorGxMWbOnBmrVq2qPP/CCy/Exz/+8Whubo799tsvpk6dGj/60Y86rfHqq6/G3LlzY//994+DDz44brzxxl2uc/7558dVV10Vp5122kC/JGAPJCZgL/bJT34yfv/738eyZcvi0UcfjY9+9KPxgQ98IDZs2BAREa+//npMnz497r///njsscfis5/9bJx//vmxevXqyhpf+tKX4oEHHoh77703VqxYEatWrYq1a9cO1ksC9kD+1VDYy8yaNSuOO+64uPjii+Pwww+PZ555JiZOnFh5/rTTTosTTzwxvva1r3V5/hlnnBFHHXVU3HDDDfHKK6/E2LFj484774xzzz03IiK2bNkSzc3N8dnPfjaWLFnS6dx//OMfMWXKlHjkkUfiuOOOG6iXCOxhhg32BoCB8fDDD0cpJY444ohOj2/bti3Gjh0bERFtbW2xePHiuPvuu+PZZ5+Nbdu2xbZt26KxsTEiIv7+97/H9u3b46STTqqcf+CBB8aRRx65+14IsMcTE7CXam9vj4aGhli7dm00NDR0em7//fePiIgbb7wxvvGNb8SSJUti6tSp0djYGPPnz4/t27dHRIQvXAK1EBOwl5o2bVq0tbXF5s2b493vfneXx/z2t7+ND3/4wzFnzpyI+F+AbNiwIY466qiIiDjssMNi+PDh8Yc//CEmT54cEREvvvhi/PWvf41TTz1197wQYI8nJmAvdcQRR0RLS0vMnTs3brzxxpg2bVo8//zz8etf/zqmTp0aH/rQh+Kwww6Le+65Jx566KE44IAD4qabbopNmzZVYmL//fePT3/60/GlL30pxo4dG+PHj4+vfvWrUV/f+Xu3t2zZEhs3boznnnsuIiKeeOKJiIiYMGFCTJgwYfe+cGC389McsBe74447Yu7cuXHJJZfEkUceGWeffXasXr06Jk2aFBERV155ZRx//PFx+umnx6xZs2LChAlxzjnndFrj+uuvj/e85z1x9tlnx2mnnRannHJKTJ8+vdMx9913X0ybNi3OOOOMiIj42Mc+FtOmTYvvfve7u+V1AoPLT3MAACm+MgEApIgJACBFTAAAKWICAEgREwBAipgAAFLEBACQIiYAgBQxAQCkiAkAIEVMAAAp/wfxB4X9KIyQ1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"lead1\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c3e460",
   "metadata": {},
   "source": [
    "#### Generate the lead2 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03d232c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='lead2'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"lead2\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b27c65a",
   "metadata": {},
   "source": [
    "#### Generate the lead3 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a43cb6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='lead3'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"lead3\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c798be",
   "metadata": {},
   "source": [
    "#### Generate the aVR feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffdd1669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aVR'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"aVR\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a013206",
   "metadata": {},
   "source": [
    "#### Generate the aVL feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a57a9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aVL'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"aVL\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6365a2a",
   "metadata": {},
   "source": [
    "#### Generate the aVF feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfbb89cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aVF'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"aVF\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73336ee4",
   "metadata": {},
   "source": [
    "#### Generate the V1 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e466086f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V1'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V1\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d12e02",
   "metadata": {},
   "source": [
    "#### Generate the V2 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68a1b68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V2'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V2\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383ddb06",
   "metadata": {},
   "source": [
    "#### Generate the V3 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c617c025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V3'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V3\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d391837",
   "metadata": {},
   "source": [
    "#### Generate the V4 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc32f3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V4'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V4\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dce8bf",
   "metadata": {},
   "source": [
    "#### Generate the V5 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70c76d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V5'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V5\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afa40a1",
   "metadata": {},
   "source": [
    "#### Generate the V6 feature bloxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "672446a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='V6'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x = df[\"V6\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55181fa",
   "metadata": {},
   "source": [
    "#### Check the balance of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35792f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Frequence  Percentage\n",
      "arrhythmia_code                       \n",
      "1                 69525000      53.960\n",
      "22                13295000      10.320\n",
      "147                6670000       5.180\n",
      "23                 5615000       4.360\n",
      "145                5225000       4.060\n",
      "105                4585000       3.560\n",
      "60                 3930000       3.050\n",
      "21                 3615000       2.810\n",
      "50                 3315000       2.570\n",
      "146                2700000       2.100\n",
      "106                2365000       1.840\n",
      "30                 1920000       1.490\n",
      "125                1005000       0.780\n",
      "120                 610000       0.470\n",
      "121                 555000       0.430\n",
      "82                  490000       0.380\n",
      "142                 480000       0.370\n",
      "51                  470000       0.360\n",
      "161                 385000       0.300\n",
      "101                 385000       0.300\n",
      "165                 320000       0.250\n",
      "104                 310000       0.240\n",
      "36                  220000       0.170\n",
      "160                 175000       0.140\n",
      "155                 140000       0.110\n",
      "108                 110000       0.090\n",
      "88                  100000       0.080\n",
      "54                   60000       0.050\n",
      "80                   45000       0.030\n",
      "83                   40000       0.030\n",
      "140                  35000       0.030\n",
      "166                  35000       0.030\n",
      "102                  25000       0.020\n",
      "148                  20000       0.020\n",
      "31                   20000       0.020\n",
      "152                  15000       0.010\n",
      "87                   15000       0.010\n",
      "37                   10000       0.010\n",
      "84                   10000       0.010\n",
      "143                   5000       0.000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class_counts = df[\"arrhythmia_code\"].value_counts()\n",
    "total_instances = class_counts.sum().compute()\n",
    "class_percentages = (class_counts / total_instances * 100).round(2)\n",
    "class_info = dd.concat([class_counts, class_percentages], axis = 1)\n",
    "class_info.columns = [\"Frequence\", \"Percentage\"]\n",
    "class_info_sorted = class_info.compute().sort_values(by = \"Frequence\", ascending = False)\n",
    "print(class_info_sorted)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f9e5ba",
   "metadata": {},
   "source": [
    "#### Conclusions:\n",
    "- There is no missing or inconsistent data.\n",
    "- Features have values on different scales. This is an indication that it may be necessary to normalize or resize the values.\n",
    "- The skewness analysis shows that all features have an asymmetric distribution. This is an indication that it may be necessary to use dataset balancing techniques.\n",
    "- The kurtosis analysis and the generated boxplots show that all features have outliers. This is an indication that it may be necessary to eliminate outliers from the dataset, if possible in this context, or use a Machine Learning model that can better deal with outliers if they were kept in the dataset.\n",
    "- The analysis of the correlation between the features shows that the features aVF, aVL, and aVR have a strong correlation (above 90%) with the features lead1 and lead3. This is an indication that the option of removing these 3 features from the dataset should be considered.\n",
    "- The dataset is quite unbalanced. There are classes that have less than 1% of instances. This is an indication that the use of balancing techniques or an alternative for regrouping instances into classes of greater granularity should be considered.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9299419b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
